Cramér-Rao Lower Bound and Estimation Algorithms For Scene-based Bistatic Radar Waveform Estimation

Abstract

Cooperative radar operations typically rely on the exchange of a limited amount of information to improve the quality of the estimated targets parameters. Unfortunately, in many instances, not all necessary information can be accessed or communicated, e.g., no line of sight (LOS) or limited resources. This problem is exacerbated with the inset of novel (irregular) waveforms, exhibiting large number of degrees of freedom, on transmit. For example, where both monostatic and bistatic measurements are available, enhanced parameter estimation can be achieved through sharing only the synchronization and geographical information between two platforms. In this paper, we focus on this scenario and derive the Cramer- Rao lower bound for the estimation of the unknown bistatic waveform under no-LOS mild assumptions on the second-order statistic of the bistatic and monostatic returns. Also, we devise a set of algorithms exploiting the monostatic estimated scene, based on spectral methods, factor analysis and calibration techniques. Through numerical experiments, we compare the performance and discuss the limitations of the introduced techniques.